Digital biotechnology

Course Coordinator

ECTS points:


Course number:

Course Description

The course is performed according to the project based learning paradigm. The content is divided into iterative blocks (modules) where each module is conceived into a mini project. Each cycle begins with lectures in which students are introduced to the biotechnological aspect of the topic, then through short lectures and exercises the digital aspect is mastered (basics of knowledge of program syntax - Python, data sources, computer tools) and finally students in groups try to run computer tools and interpret results. In addition to basic knowledge of relevant databases (patent, biotechnology, bioinformatics), students would be introduced to the most used computer tools in order to get involved in research work in biotechnology as soon as possible. The great advantage of this course is that the course relies entirely on computer resources, so that early involvement of students in research and project work is easier to do.

M1 Introduction to the most commonly used formats for storage and retrieval using biological sequences in biotechnological databases (FASTA, PDB, SCF and FASTQ)

M2 Access to digital data in biotechnology (ENTREZ, SRS, Bioython)

M3 Importance of innovation in biotechnology (

M4 Examples of the contribution of digital technology in biotechnology (main challenges in the pharmaceutical and biotechnology industries, how data and their analysis can affect the transformation of these industries)

M5 Agility and communication skills in biotechnology - presentation of own results and peer review

M6 Data assessment, selection of techniques and decision making on the course of the project - on the example of own projects of the Laboratory for Bioinformatics

M7 Example of a mini digital biotechnology project - working with specific data through the Kbase platform and Python scripts


Big Data: A Revolution That Will Transform How We Live, Work and Think. Viktor Mayer-Schnberger and Kenneth Cukier

Storytelling With Data: A Data Visualization Guide for Business Professionals

Book by Cole Nussbaumer Knaflic

Genentech: The Beginnings of Biotech. Sally Smith Hughes

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

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